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Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1061–1070 of 1808 papers

Title	Date	Tasks	Status	Hype
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case	Apr 16, 2022	Adversarial AttackFederated Learning	—Unverified	0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks	Apr 14, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization	Apr 11, 2022	Adversarial AttackObject	—Unverified	0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning	Apr 5, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition	Apr 4, 2022	Activity RecognitionAdversarial Attack	—Unverified	0
Adversarial Neon Beam: A Light-based Physical Attack to DNNs	Apr 2, 2022	Adversarial Attack	—Unverified	0
Zero-Query Transfer Attacks on Context-Aware Object Detectors	Mar 29, 2022	Adversarial AttackObject	—Unverified	0
Exploring Frequency Adversarial Attacks for Face Forgery Detection	Mar 29, 2022	Adversarial AttackMeta-Learning	—Unverified	0
Boosting Black-Box Adversarial Attacks with Meta Learning	Mar 28, 2022	Adversarial AttackMeta-Learning	—Unverified	0
Text Adversarial Purification as Defense against Adversarial Attacks	Mar 27, 2022	Adversarial AttackAdversarial Defense	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified